OpenRaiser/Intern-Atlas huggingface dataset:
Effectus of Quantum Probability on Relational Structures
Venue: arXiv cs.LO 2020
Year: 2020

The notion of effectus from categorical logic is relevant in the emerging field of categorical probability theory. In some cases, stochastic maps are represented by maps in the Kleisli category of some probability monad. Quantum homomorphisms from combinatorics and quantum information theory are the Kleisli maps of certain sort of quantum monad. We show that the Kleisli category of this quantum monad is an effectus. This gives rise to notions of quantum probabilistic reasoning as predicates, validity, conditioning, and channels.
The AVA-Kinetics Localized Human Actions Video Dataset
Venue: arXiv cs.CV 2020
Year: 2020

This paper describes the AVA-Kinetics localized human actions video dataset. The dataset is collected by annotating videos from the Kinetics-700 dataset using the AVA annotation protocol, and extending the original AVA dataset with these new AVA annotated Kinetics clips. The dataset contains over 230k clips annotated with the 80 AVA action classes for each of the humans in key-frames. We describe the annotation process and provide statistics about the new dataset. We also include a baseline evaluation using the Video Action Transformer Network on the AVA-Kinetics dataset, demonstrating improved performance for action classification on the AVA test set. The dataset can be downloaded from https://research.google.com/ava/
Cross-modal Language Generation using Pivot Stabilization for Web-scale Language Coverage
Venue: arXiv cs.CL 2020
Year: 2020

Cross-modal language generation tasks such as image captioning are directly hurt in their ability to support non-English languages by the trend of data-hungry models combined with the lack of non-English annotations. We investigate potential solutions for combining existing language-generation annotations in English with translation capabilities in order to create solutions at web-scale in both domain and language coverage. We describe an approach called Pivot-Language Generation Stabilization (PLuGS), which leverages directly at training time both existing English annotations (gold data) as well as their machine-translated versions (silver data); at run-time, it generates first an English caption and then a corresponding target-language caption. We show that PLuGS models outperform other candidate solutions in evaluations performed over 5 different target languages, under a large-domain testset using images from the Open Images dataset. Furthermore, we find an interesting effect where the English captions generated by the PLuGS models are better than the captions generated by the original, monolingual English model.
Age-Optimal UAV Scheduling for Data Collectionwith Battery Recharging
Venue: arXiv cs.NI 2020
Year: 2020

We study route scheduling of a UAV for data collec-tion from remote sensor nodes (SNs) with battery recharging. Thefreshness of the collected information is captured by the metric ofage of information (AoI). The objective is to minimize the averageAoI cost of all SNs over a scheduling time horizon. We prove thatthe problem is NP-hard via a reduction from the Hamiltonianpath. Next, we prove tractability of the problem for a symmetricscenario. For problem solving, we develop an algorithm based ongraph labeling. Finally, we show the effectiveness of our algorithmin comparison to greedy scheduling.
A direct proof of Agafonov's theorem and an extension to shift of finite type
Venue: arXiv cs.FL 2020
Year: 2020

We provide a direct proof of Agafonov's theorem which states that finite state selection preserves normality. We also extends this result to the more general setting of shifts of finite type by defining selections which are compatible the shift. A slightly more general statement is obtained as we show that any Markov measure is preserved by finite state compatible selection.
Towards Controllable Biases in Language Generation
Venue: arXiv cs.CL 2020
Year: 2020

We present a general approach towards controllable societal biases in natural language generation (NLG). Building upon the idea of adversarial triggers, we develop a method to induce societal biases in generated text when input prompts contain mentions of specific demographic groups. We then analyze two scenarios: 1) inducing negative biases for one demographic and positive biases for another demographic, and 2) equalizing biases between demographics. The former scenario enables us to detect the types of biases present in the model. Specifically, we show the effectiveness of our approach at facilitating bias analysis by finding topics that correspond to demographic inequalities in generated text and comparing the relative effectiveness of inducing biases for different demographics. The second scenario is useful for mitigating biases in downstream applications such as dialogue generation. In our experiments, the mitigation technique proves to be effective at equalizing the amount of biases across demographics while simultaneously generating less negatively biased text overall.
Unsupervised Transfer of Semantic Role Models from Verbal to Nominal Domain
Venue: arXiv cs.CL 2020
Year: 2020

Semantic role labeling (SRL) is an NLP task involving the assignment of predicate arguments to types, called semantic roles. Though research on SRL has primarily focused on verbal predicates and many resources available for SRL provide annotations only for verbs, semantic relations are often triggered by other linguistic constructions, e.g., nominalizations. In this work, we investigate a transfer scenario where we assume role-annotated data for the source verbal domain but only unlabeled data for the target nominal domain. Our key assumption, enabling the transfer between the two domains, is that selectional preferences of a role (i.e., preferences or constraints on the admissible arguments) do not strongly depend on whether the relation is triggered by a verb or a noun. For example, the same set of arguments can fill the Acquirer role for the verbal predicate `acquire' and its nominal form `acquisition'. We approach the transfer task from the variational autoencoding perspective. The labeler serves as an encoder (predicting role labels given a sentence), whereas selectional preferences are captured in the decoder component (generating arguments for the predicting roles). Nominal roles are not labeled in the training data, and the learning objective instead pushes the labeler to assign roles predictive of the arguments. Sharing the decoder parameters across the domains encourages consistency between labels predicted for both domains and facilitates the transfer. The method substantially outperforms baselines, such as unsupervised and `direct transfer' methods, on the English CoNLL-2009 dataset.
Automatically Eliminating Speculative Leaks from Cryptographic Code with Blade
Venue: arXiv cs.CR 2020
Year: 2020

We introduce BLADE, a new approach to automatically and efficiently eliminate speculative leaks from cryptographic code. BLADE is built on the insight that to stop leaks via speculation, it suffices to $\textit{cut}$ the dataflow from expressions that speculatively introduce secrets ($\textit{sources}$) to those that leak them through the cache ($\textit{sinks}$), rather than prohibit speculation altogether. We formalize this insight in a $\textit{static type system}$ that (1) types each expression as either $\textit{transient}$, i.e., possibly containing speculative secrets or as being $\textit{stable}$, and (2) prohibits speculative leaks by requiring that all $\textit{sink}$ expressions are stable. BLADE relies on a new new abstract primitive, $\textbf{protect}$, to halt speculation at fine granularity. We formalize and implement $\textbf{protect}$ using existing architectural mechanisms, and show how BLADE's type system can automatically synthesize a $\textit{minimal}$ number of $\textbf{protect}$s to provably eliminate speculative leaks. We implement BLADE in the Cranelift WebAssembly compiler and evaluate our approach by repairing several verified, yet vulnerable WebAssembly implementations of cryptographic primitives. We find that Blade can fix existing programs that leak via speculation $\textit{automatically}$, without user intervention, and $\textit{efficiently}$ even when using fences to implement $\textbf{protect}$.
Using monodromy to statistically estimate the number of solutions
Venue: arXiv cs.RO 2020
Year: 2020

Synthesis problems for linkages in kinematics often yield large structured parameterized polynomial systems which generically have far fewer solutions than traditional upper bounds would suggest. This paper describes statistical models for estimating the generic number of solutions of such parameterized polynomial systems. The new approach extends previous work on success ratios of parameter homotopies to using monodromy loops as well as the addition of a trace test that provides a stopping criterion for validating that all solutions have been found. Several examples are presented demonstrating the method including Watt I six-bar motion generation problems.
The EPIC-KITCHENS Dataset: Collection, Challenges and Baselines
Venue: arXiv cs.CV 2020
Year: 2020

Since its introduction in 2018, EPIC-KITCHENS has attracted attention as the largest egocentric video benchmark, offering a unique viewpoint on people's interaction with objects, their attention, and even intention. In this paper, we detail how this large-scale dataset was captured by 32 participants in their native kitchen environments, and densely annotated with actions and object interactions. Our videos depict nonscripted daily activities, as recording is started every time a participant entered their kitchen. Recording took place in 4 countries by participants belonging to 10 different nationalities, resulting in highly diverse kitchen habits and cooking styles. Our dataset features 55 hours of video consisting of 11.5M frames, which we densely labelled for a total of 39.6K action segments and 454.2K object bounding boxes. Our annotation is unique in that we had the participants narrate their own videos after recording, thus reflecting true intention, and we crowd-sourced ground-truths based on these. We describe our object, action and. anticipation challenges, and evaluate several baselines over two test splits, seen and unseen kitchens. We introduce new baselines that highlight the multimodal nature of the dataset and the importance of explicit temporal modelling to discriminate fine-grained actions e.g. 'closing a tap' from 'opening' it up.
Generative Adversarial Data Programming
Venue: arXiv cs.CV 2020
Year: 2020

The paucity of large curated hand-labeled training data forms a major bottleneck in the deployment of machine learning models in computer vision and other fields. Recent work (Data Programming) has shown how distant supervision signals in the form of labeling functions can be used to obtain labels for given data in near-constant time. In this work, we present Adversarial Data Programming (ADP), which presents an adversarial methodology to generate data as well as a curated aggregated label, given a set of weak labeling functions. More interestingly, such labeling functions are often easily generalizable, thus allowing our framework to be extended to different setups, including self-supervised labeled image generation, zero-shot text to labeled image generation, transfer learning, and multi-task learning.
Multi-View Self-Attention for Interpretable Drug-Target Interaction Prediction
Venue: arXiv cs.LG 2020
Year: 2020

The drug discovery stage is a vital aspect of the drug development process and forms part of the initial stages of the development pipeline. In recent times, machine learning-based methods are actively being used to model drug-target interactions for rational drug discovery due to the successful application of these methods in other domains. In machine learning approaches, the numerical representation of molecules is critical to the performance of the model. While significant progress has been made in molecular representation engineering, this has resulted in several descriptors for both targets and compounds. Also, the interpretability of model predictions is a vital feature that could have several pharmacological applications. In this study, we propose a self-attention-based multi-view representation learning approach for modeling drug-target interactions. We evaluated our approach using three benchmark kinase datasets and compared the proposed method to some baseline models. Our experimental results demonstrate the ability of our method to achieve competitive prediction performance and offer biologically plausible drug-target interaction interpretations.
Automatic Discourse Segmentation: Review and Perspectives
Venue: arXiv cs.IR 2020
Year: 2020

Multilingual discourse parsing is a very prominent research topic. The first stage for discourse parsing is discourse segmentation. The study reported in this article addresses a review of two on-line available discourse segmenters (for English and Portuguese). We evaluate the possibility of developing similar discourse segmenters for Spanish, French and African languages.
ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations
Venue: arXiv cs.CL 2020
Year: 2020

In order to simplify a sentence, human editors perform multiple rewriting transformations: they split it into several shorter sentences, paraphrase words (i.e. replacing complex words or phrases by simpler synonyms), reorder components, and/or delete information deemed unnecessary. Despite these varied range of possible text alterations, current models for automatic sentence simplification are evaluated using datasets that are focused on a single transformation, such as lexical paraphrasing or splitting. This makes it impossible to understand the ability of simplification models in more realistic settings. To alleviate this limitation, this paper introduces ASSET, a new dataset for assessing sentence simplification in English. ASSET is a crowdsourced multi-reference corpus where each simplification was produced by executing several rewriting transformations. Through quantitative and qualitative experiments, we show that simplifications in ASSET are better at capturing characteristics of simplicity when compared to other standard evaluation datasets for the task. Furthermore, we motivate the need for developing better methods for automatic evaluation using ASSET, since we show that current popular metrics may not be suitable when multiple simplification transformations are performed.
A Holistic Investigation on Terahertz Propagation and Channel Modeling Toward Vertical Heterogeneous Networks
Venue: arXiv cs.IT 2020
Year: 2020

User-centric and low latency communications can be enabled not only by small cells but also through ubiquitous connectivity. Recently, the vertical heterogeneous network (V-HetNet) architecture is proposed to backhaul/fronthaul a large number of small cells. Like an orchestra, the V-HetNet is a polyphony of different communication ensembles, including geostationary orbit (GEO), and low-earth orbit (LEO) satellites (e.g., CubeSats), and networked flying platforms (NFPs) along with terrestrial communication links. In this study, we propose the Terahertz (THz) communications to enable the elements of V-HetNets to function in harmony. As THz links offer a large bandwidth, leading to ultra-high data rates, it is suitable for backhauling and fronthauling small cells. Furthermore, THz communications can support numerous applications from inter-satellite links to in-vivo nanonetworks. However, to savor this harmony, we need accurate channel models. In this paper, the insights obtained through our measurement campaigns are highlighted, to reveal the true potential of THz communications in V-HetNets.
SciREX: A Challenge Dataset for Document-Level Information Extraction
Venue: arXiv cs.CL 2020
Year: 2020

Extracting information from full documents is an important problem in many domains, but most previous work focus on identifying relationships within a sentence or a paragraph. It is challenging to create a large-scale information extraction (IE) dataset at the document level since it requires an understanding of the whole document to annotate entities and their document-level relationships that usually span beyond sentences or even sections. In this paper, we introduce SciREX, a document level IE dataset that encompasses multiple IE tasks, including salient entity identification and document level $N$-ary relation identification from scientific articles. We annotate our dataset by integrating automatic and human annotations, leveraging existing scientific knowledge resources. We develop a neural model as a strong baseline that extends previous state-of-the-art IE models to document-level IE. Analyzing the model performance shows a significant gap between human performance and current baselines, inviting the community to use our dataset as a challenge to develop document-level IE models. Our data and code are publicly available at https://github.com/allenai/SciREX
Clinical Reading Comprehension: A Thorough Analysis of the emrQA Dataset
Venue: arXiv cs.CL 2020
Year: 2020

Machine reading comprehension has made great progress in recent years owing to large-scale annotated datasets. In the clinical domain, however, creating such datasets is quite difficult due to the domain expertise required for annotation. Recently, Pampari et al. (EMNLP'18) tackled this issue by using expert-annotated question templates and existing i2b2 annotations to create emrQA, the first large-scale dataset for question answering (QA) based on clinical notes. In this paper, we provide an in-depth analysis of this dataset and the clinical reading comprehension (CliniRC) task. From our qualitative analysis, we find that (i) emrQA answers are often incomplete, and (ii) emrQA questions are often answerable without using domain knowledge. From our quantitative experiments, surprising results include that (iii) using a small sampled subset (5%-20%), we can obtain roughly equal performance compared to the model trained on the entire dataset, (iv) this performance is close to human expert's performance, and (v) BERT models do not beat the best performing base model. Following our analysis of the emrQA, we further explore two desired aspects of CliniRC systems: the ability to utilize clinical domain knowledge and to generalize to unseen questions and contexts. We argue that both should be considered when creating future datasets.
Pointer-Machine Algorithms for Fully-Online Construction of Suffix Trees and DAWGs on Multiple Strings
Venue: arXiv cs.DS 2020
Year: 2020

We deal with the problem of maintaining the suffix tree indexing structure for a fully-online collection of multiple strings, where a new character can be prepended to any string in the collection at any time. The only previously known algorithm for the problem, recently proposed by Takagi et al. [Algorithmica 82(5): 1346-1377 (2020)], runs in $O(N \log σ)$ time and $O(N)$ space on the word RAM model, where $N$ denotes the total length of the strings and $σ$ denotes the alphabet size. Their algorithm makes heavy use of the nearest marked ancestor (NMA) data structure on semi-dynamic trees, that can answer queries and supports insertion of nodes in $O(1)$ amortized time on the word RAM model. In this paper, we present a simpler fully-online right-to-left algorithm that builds the suffix tree for a given string collection in $O(N (\log σ+ \log d))$ time and $O(N)$ space, where $d$ is the maximum number of in-coming Weiner links to a node of the suffix tree. We note that $d$ is bounded by the height of the suffix tree, which is further bounded by the length of the longest string in the collection. The advantage of this new algorithm is that it works on the pointer machine model, namely, it does not use the complicated NMA data structures that involve table look-ups. As a byproduct, we also obtain a pointer-machine algorithm for building the directed acyclic word graph (DAWG) for a fully-online left-to-right collection of multiple strings, which runs in $O(N (\log σ+ \log d))$ time and $O(N)$ space again without the aid of the NMA data structures.
Independent Set on P$_k$-Free Graphs in Quasi-Polynomial Time
Venue: arXiv cs.DS 2020
Year: 2020

We present an algorithm that takes as input a graph $G$ with weights on the vertices, and computes a maximum weight independent set $S$ of $G$. If the input graph $G$ excludes a path $P_k$ on $k$ vertices as an induced subgraph, the algorithm runs in time $n^{O(k^2 \log^3 n)}$. Hence, for every fixed $k$ our algorithm runs in quasi-polynomial time. This resolves in the affirmative an open problem of [Thomassé, SODA'20 invited presentation]. Previous to this work, polynomial time algorithms were only known for $P_4$-free graphs [Corneil et al., DAM'81], $P_5$-free graphs [Lokshtanov et al., SODA'14], and $P_6$-free graphs [Grzesik et al., SODA'19]. For larger values of $t$, only $2^{O(\sqrt{kn\log n})}$ time algorithms [Bascó et al., Algorithmica'19] and quasi-polynomial time approximation schemes [Chudnovsky et al., SODA'20] were known. Thus, our work is the first to offer conclusive evidence that Independent Set on $P_k$-free graphs is not NP-complete for any integer $k$. Additionally we show that for every graph $H$, if there exists a quasi-polynomial time algorithm for Independent Set on $C$-free graphs for every connected component $C$ of $H$, then there also exists a quasi-polynomial time algorithm for {\sc Independent Set} on $H$-free graphs. This lifts our quasi-polynomial time algorithm to $T_k$-free graphs, where $T_k$ has one component that is a $P_k$, and $k-1$ components isomorphic to a fork (the unique $5$-vertex tree with a degree $3$ vertex).
TEX-CUP: The University of Texas Challenge for Urban Positioning
Venue: arXiv cs.RO 2020
Year: 2020

A public benchmark dataset collected in the dense urban center of the city of Austin, TX is introduced for evaluation of multi-sensor GNSS-based urban positioning. Existing public datasets on localization and/or odometry evaluation are based on sensors such as lidar, cameras, and radar. The role of GNSS in these datasets is typically limited to the generation of a reference trajectory in conjunction with a high-end inertial navigation system (INS). In contrast, the dataset introduced in this paper provides raw ADC output of wideband intermediate frequency (IF) GNSS data along with tightly synchronized raw measurements from inertial measurement units (IMUs) and a stereoscopic camera unit. This dataset will enable optimization of the full GNSS stack from signal tracking to state estimation, as well as sensor fusion with other automotive sensors. The dataset is available at http://radionavlab.ae.utexas.edu under Public Datasets. Efforts to collect and share similar datasets from a number of dense urban centers around the world are under way.
Hard-Coded Gaussian Attention for Neural Machine Translation
Venue: arXiv cs.CL 2020
Year: 2020

Recent work has questioned the importance of the Transformer's multi-headed attention for achieving high translation quality. We push further in this direction by developing a "hard-coded" attention variant without any learned parameters. Surprisingly, replacing all learned self-attention heads in the encoder and decoder with fixed, input-agnostic Gaussian distributions minimally impacts BLEU scores across four different language pairs. However, additionally hard-coding cross attention (which connects the decoder to the encoder) significantly lowers BLEU, suggesting that it is more important than self-attention. Much of this BLEU drop can be recovered by adding just a single learned cross attention head to an otherwise hard-coded Transformer. Taken as a whole, our results offer insight into which components of the Transformer are actually important, which we hope will guide future work into the development of simpler and more efficient attention-based models.